app.py

# https://huggingface.co/spaces/flintrepa/audio_pdf_summary

import pdfproc

import gradio as gr
from transformers import pipeline, AutoProcessor, AutoModel


def pdf_summary(file_obj):
    print(file_obj)
    # Read pdf
    text_per_page = pdfproc.read_pdf(file_obj)

    # Extract Abstract from pdf
    i = text_per_page['Page_0'][0].index('Abstract\n')
    abstract = text_per_page['Page_0'][0][i+1]
    abstract = abstract.replace('-\n', '').replace('\n', ' ')
    print(f"\nABSTRACT\n-----\n{abstract}\n-----\n")

    # Summarize the abstract
    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
    summary = summarizer(abstract, min_length=1, max_length=20, do_sample=False)[0]["summary_text"]
    print(f"\nSUMMARY\n-----\n{summary}\n-----\n")

    # Text-to-Speech
    processor = AutoProcessor.from_pretrained("suno/bark-small")
    model = AutoModel.from_pretrained("suno/bark-small")
    inputs = processor(
        text=summary,
        return_tensors="pt",
    )
    speech_values = model.generate(**inputs, do_sample=True)
    sampling_rate = model.generation_config.sample_rate
    print("\nAUDIO GENERATED\n")

    return sampling_rate, speech_values.cpu().numpy().squeeze()


if __name__ == "__main__":

    example_file_name = './hidden-debt-article.pdf'
    iface = gr.Interface(
        fn=pdf_summary, 
        inputs="file", 
        outputs="audio",
        title="Audio PDF summary",
        description="Upload a PDF file with an Abstract to get an audio summary of it",
        examples=[example_file_name]
        )
    iface.launch()